Last modified: June 11, 2018
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Isolation is a fundamental concept in database systems that ensures each transaction operates independently without interfering with others. When multiple transactions occur simultaneously, isolation guarantees that the operations within one transaction are not visible to other transactions until they are completed and committed. This means that the intermediate states of a transaction are hidden from others, maintaining data integrity and consistency throughout the process.
Think of isolation as separate workspaces for each transaction. Imagine several chefs in a kitchen, each preparing their own dish without disturbing or altering the ingredients of others. Only when a chef finishes and serves the dish do others see the final result.
+----------------------+ +----------------------+
| Transaction A | | Transaction B |
| - Processes Data | | - Processes Data |
| - No Interference | | - No Interference |
+----------------------+ +----------------------+After reading the material, you should be able to answer the following questions:
Isolation plays a critical role in ensuring that concurrent transactions do not lead to data anomalies or inconsistencies. By isolating transactions, the database system prevents issues like dirty reads, non-repeatable reads, and phantom reads, which can compromise data integrity.
In a multi-user environment, numerous transactions might be happening at the same time. Isolation ensures that these concurrent transactions do not interfere with each other, allowing each to proceed as if it were the only one interacting with the database.
By keeping transactions isolated until they are completed, the database maintains accurate and consistent data. This prevents unintended side effects that could occur if transactions were allowed to see uncommitted changes from others.
To better understand how isolation works, let's look at some practical scenarios where isolation is essential.
Consider a banking system where Transaction T1 is transferring money from Account X to Account Y. This involves debiting Account X and crediting Account Y.
Transaction T1:
Simultaneously, Transaction T2 wants to read the balance of Account Y.
Imagine an inventory system where Transaction T1 reads the stock level of a product twice during its operation.
Transaction T1:
At the same time, Transaction T2 sells some units of Product A and updates the stock level.
In an online booking system, two users attempt to reserve the last available seat.
Transaction T1:
Transaction T2:
What happens?
Databases provide different isolation levels to balance between data integrity and system performance. Higher isolation levels offer more data consistency but can reduce concurrency and increase resource usage.
| Isolation Level | Description | Pros | Cons |
| Read Uncommitted | Transactions can read uncommitted changes made by others (dirty reads). | Highest concurrency, minimal locking. | Risk of dirty reads, data inconsistencies. |
| Read Committed | Transactions only see committed changes. | Prevents dirty reads, good performance. | May experience non-repeatable reads. |
| Repeatable Read | Ensures that if data is read twice, it remains the same during the transaction. | Prevents non-repeatable reads. | Possible phantom reads, reduced concurrency. |
| Serializable | Transactions are completely isolated from each other. | Highest data integrity. | Lowest concurrency, highest resource usage. |
At this level, a transaction may read data that has been modified but not yet committed by other transactions. This can lead to dirty reads, where a transaction reads uncommitted changes that might be rolled back.
Transactions only see data that has been committed. This prevents dirty reads but allows non-repeatable reads, where data read twice might change if another transaction modifies it between reads.
This level ensures that if a transaction reads data multiple times, it sees the same data each time. It prevents non-repeatable reads but may still allow phantom reads, where new rows added by other transactions are visible.
The strictest isolation level, where transactions are completely isolated. It prevents dirty reads, non-repeatable reads, and phantom reads by locking the data range involved, but it can significantly reduce concurrency.
To enforce isolation, databases use various concurrency control techniques that manage how transactions interact with shared data.
Locking is a primary method where transactions acquire locks on data before accessing it.
Locks can be applied at different granularities:
By controlling access through locks, the database ensures that transactions do not interfere with each other's operations.
Optimistic concurrency control assumes that transaction conflicts are rare and allows transactions to proceed without locking resources.
Process:
This approach reduces the overhead of locking but requires a mechanism to detect and resolve conflicts during the commit phase.
MVCC allows multiple versions of data to exist simultaneously, enabling transactions to access data without waiting for locks.
Benefits:
Databases like PostgreSQL and Oracle use MVCC to enhance concurrency while maintaining isolation.
Understanding how isolation works can be aided by visual diagrams.
[Transaction T1] [Transaction T2]
| |
[Begin Transaction] [Begin Transaction]
| |
[Update Data Item X] |
| [Read Data Item X]
| |
[Commit Transaction] |
|
[Read Data Item X]
|
[Commit Transaction]In this scenario:
SQL databases implement isolation to ensure that concurrent transactions execute in a controlled and consistent manner. Isolation levels dictate how transactions interact with each other, specifically in terms of visibility and accessibility of data being read or modified. These settings are crucial in preventing common concurrency issues such as dirty reads, non-repeatable reads, and phantom reads.
When configuring isolation, you can choose between different levels such as READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, and SERIALIZABLE. Each level represents a trade-off between data consistency and system performance. For example, SERIALIZABLE offers the highest level of isolation but at the cost of increased resource contention, while READ UNCOMMITTED allows maximum concurrency with the risk of reading uncommitted or intermediate data.
Isolation levels can be applied either for a single session or an individual transaction. By setting an isolation level, you define the boundaries for interaction between concurrent transactions. In the example below, the SERIALIZABLE isolation level ensures that the transaction operates in complete isolation, treating it as if it were the only transaction running.
-- Setting the isolation level to Serializable
SET TRANSACTION ISOLATION LEVEL SERIALIZABLE;
BEGIN TRANSACTION;
-- Transaction operations here
COMMIT;This configuration guarantees that no other transactions can read or write data that is being processed until the current transaction completes. It is ideal for scenarios that require the strictest consistency guarantees, such as financial calculations.
Dirty reads occur when a transaction reads uncommitted changes made by another transaction. The following example demonstrates how the READ COMMITTED isolation level prevents this issue. Transaction T2 cannot access the intermediate state of data modified by T1 until T1 commits.
-- Transaction T1
BEGIN TRANSACTION;
UPDATE accounts
SET balance = balance - 500
WHERE account_id = 1;
-- Transaction T2
SET TRANSACTION ISOLATION LEVEL READ COMMITTED;
BEGIN TRANSACTION;
SELECT balance
FROM accounts
WHERE account_id = 1;
COMMIT;
-- T2 will not see the uncommitted change from T1 due to the Read Committed isolation level.
-- T1 commits the transaction
COMMIT;In this case, T2's query only retrieves committed data, ensuring data integrity and avoiding inconsistencies caused by partially completed operations in T1.
Some database systems allow you to use explicit locking hints to manage transaction concurrency. Locking hints provide fine-grained control over how rows are locked during query execution. For example, the WITH (UPDLOCK) hint requests an update lock on the specified row, ensuring that other transactions cannot modify the row until the lock is released.
SELECT *
FROM accounts WITH (UPDLOCK)
WHERE account_id = 1;By applying an update lock, the query prevents write conflicts and enforces orderly access to the data, reducing the risk of race conditions. This approach is particularly useful in high-concurrency environments where multiple transactions may attempt to update the same rows simultaneously.